AI-Driven Bioinformatics: From Omics Data to Biological Insights
Uncover Biological Insights Using AI-Powered Omics Data Analysis
About This Course
Omics data has revolutionized biological research by enabling the large-scale study of genes, proteins, metabolites, and their complex interactions. However, handling and interpreting this vast amount of data can be daunting. AI and machine learning offer powerful approaches to uncover hidden patterns, make predictions, and gain insights that were previously unattainable using traditional methods. By integrating AI techniques with bioinformatics tools, researchers can accelerate the discovery of biomarkers, disease pathways, and therapeutic targets.
This workshop focuses on the AI-driven analysis of omics data, providing hands-on experience in using tools like Python, R, and key machine learning libraries (Scikit-learn, TensorFlow, Keras) to analyze large biological datasets. Participants will explore various applications in genomics, transcriptomics, and proteomics, learning to apply AI algorithms for clustering, classification, and regression tasks. Additionally, the workshop will highlight the use of AI in personalized medicine and systems biology for biological discovery.
Aim
This workshop aims to equip participants with the skills to apply AI and machine learning in bioinformatics, specifically focusing on omics data analysis. Participants will learn how to process, analyze, and extract biological insights from genomics, transcriptomics, proteomics, and metabolomics data using modern AI techniques. The program will help bridge the gap between computational tools and biological applications, empowering participants to uncover patterns, predict disease mechanisms, and advance precision medicine.
Workshop Objectives
- Understand key AI techniques used in bioinformatics and omics data analysis.
- Preprocess, clean, and prepare omics datasets (genomics, transcriptomics, proteomics).
- Apply machine learning models (clustering, classification, regression) to omics data.
- Extract biological insights and predict disease mechanisms using AI.
- Understand the role of AI in precision medicine and systems biology.
Workshop Structure
Day 1: Introduction to AI in Omics Data Analysis
- Types of omics data (Genomics, Transcriptomics, Proteomics, Metabolomics) and challenges
- Common formats (FASTQ, VCF, GFF, Fasta), preprocessing steps, quality control tools
- Introduction to AI concepts, machine learning, deep learning, and their applications in bioinformatics
- Cleaning, normalization, and transformation of omics data, handling missing values and outliers
- Tools: Python, Pandas, NumPy, Biopython, Scikit-learn, Jupyter/Colab
Day 2: Feature Engineering and Model Building
- Principal Component Analysis (PCA), t-SNE, feature selection techniques for omics data
- Regression, classification, and clustering algorithms; applications in gene expression analysis, biomarker discovery, and disease prediction
- Clustering techniques (K-means, Hierarchical clustering, DBSCAN) for identifying patterns in high-dimensional data
- Cross-validation, ROC-AUC, Precision-Recall, confusion matrix, F1-score
- Tools: Scikit-learn, XGBoost, LightGBM, Seaborn/Matplotlib, Jupyter/Colab
Day 3: Advanced AI Techniques and Biological Insights
- Introduction to Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders for omics data interpretation
- AI models for identifying key genes, regulatory networks, and gene-environment interactions
- Using AI to uncover biological pathways, molecular interactions, and druggable targets
- GridSearchCV, RandomizedSearchCV for model performance enhancement
- Building and evaluating models, generating insights, and preparing reproducible reports
- Tools: TensorFlow/Keras, PyTorch, Scikit-learn, Seaborn/Matplotlib, Jupyter/Colab, (optional) Streamlit for lightweight deployment
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/10/2026
IST 07:00 PM
Workshop Dates
02/10/2026 – 02/12/2026
IST 08:00 PM
Workshop Outcomes
Participants will be able to:
- Preprocess and clean omics datasets for machine learning analysis.
- Apply clustering, classification, and regression models to genomic and proteomic data.
- Interpret model outputs to extract meaningful biological insights.
- Build predictive models for disease classification and biomarker discovery.
- Utilize AI in precision medicine and systems biology for better health outcomes.
Fee Structure
Student Fee
₹1799 | $65
Ph.D. Scholar / Researcher Fee
₹2799 | $75
Academician / Faculty Fee
₹3799 | $85
Industry Professional Fee
₹4799 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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